ArticleJournal of cancer research and clinical oncology2018
Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.
Article in Journal of cancer research and clinical oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Update on skin cancer prevention modalities and screening protocols in solid organ transplant recipients: a scoping review.Archives of dermatological research · 2024Article
- Review
- Refining skin lesions classification performance using geometric features of superpixels.Scientific reports · 2023Article
- AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.Cancers · 2023Review
- The promise of machine learning applications in solid organ transplantation.NPJ digital medicine · 2022Review
- Massive transfusion prediction in patients with multiple trauma by decision tree: a retrospective analysis.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2021Article
- Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach.JMIR medical informatics · 2020Article
- Phosphoinositide 3-kinase-delta could be a biomarker for eosinophilic nasal polyps.Scientific reports · 2018Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNon-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients.
methodsWe performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984).
resultsNMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI < 40 who did not receive sirolimus, as high risk (7.3% cumulative incidence of NMSC). The predictions in the derivation set were almost identical to those in the validation set (r
conclusionsThe decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.
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